AI-ACCELERATED DRUG DISCOVERY
Available from Reaxense
This protein is integrated into the Receptor.AI ecosystem as a prospective target with high therapeutic potential. We performed a comprehensive characterization of Krueppel-like factor 6 including:
1. LLM-powered literature research
Our custom-tailored LLM extracted and formalized all relevant information about the protein from a large set of structured and unstructured data sources and stored it in the form of a Knowledge Graph. This comprehensive analysis allowed us to gain insight into Krueppel-like factor 6 therapeutic significance, existing small molecule ligands, relevant off-targets, and protein-protein interactions.
Fig. 1. Preliminary target research workflow
2. AI-Driven Conformational Ensemble Generation
Starting from the initial protein structure, we employed advanced AI algorithms to predict alternative functional states of Krueppel-like factor 6, including large-scale conformational changes along "soft" collective coordinates. Through molecular simulations with AI-enhanced sampling and trajectory clustering, we explored the broad conformational space of the protein and identified its representative structures. Utilizing diffusion-based AI models and active learning AutoML, we generated a statistically robust ensemble of equilibrium protein conformations that capture the receptor's full dynamic behavior, providing a robust foundation for accurate structure-based drug design.
Fig. 2. AI-powered molecular dynamics simulations workflow
3. Binding pockets identification and characterization
We employed the AI-based pocket prediction module to discover orthosteric, allosteric, hidden, and cryptic binding pockets on the protein’s surface. Our technique integrates the LLM-driven literature search and structure-aware ensemble-based pocket detection algorithm that utilizes previously established protein dynamics. Tentative pockets are then subject to AI scoring and ranking with simultaneous detection of false positives. In the final step, the AI model assesses the druggability of each pocket enabling a comprehensive selection of the most promising pockets for further targeting.
Fig. 3. AI-based binding pocket detection workflow
4. AI-Powered Virtual Screening
Our ecosystem is equipped to perform AI-driven virtual screening on Krueppel-like factor 6. With access to a vast chemical space and cutting-edge AI docking algorithms, we can rapidly and reliably predict the most promising, novel, diverse, potent, and safe small molecule ligands of Krueppel-like factor 6. This approach allows us to achieve an excellent hit rate and to identify compounds ready for advanced lead discovery and optimization.
Fig. 4. The screening workflow of Receptor.AI
Receptor.AI, in partnership with Reaxense, developed a next-generation technology for on-demand focused library design to enable extensive target exploration.
The focused library for Krueppel-like factor 6 includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.
Krueppel-like factor 6
partner:
Reaxense
upacc:
Q99612
UPID:
KLF6_HUMAN
Alternative names:
B-cell-derived protein 1; Core promoter element-binding protein; GC-rich sites-binding factor GBF; Proto-oncogene BCD1; Suppressor of tumorigenicity 12 protein; Transcription factor Zf9
Alternative UPACC:
Q99612; B2RE86; B4DDN0; D3DRR1; F5H3M5; Q5VUT7; Q5VUT8; Q9BT79
Background:
Krueppel-like factor 6 (KLF6), also known as B-cell-derived protein 1 and Transcription factor Zf9, plays a pivotal role in cell growth, development, and differentiation. It functions as a transcriptional activator, binding specifically to GC box motifs, which are crucial elements in the promoter regions of various genes. This protein's involvement in B-cell growth and development highlights its significance in cellular regulatory mechanisms.
Therapeutic significance:
KLF6 is implicated in the pathogenesis of several malignancies, including gastric and prostate cancers. These associations are due to variants affecting the gene encoding KLF6, underscoring its potential as a target for therapeutic intervention. Understanding the role of KLF6 in these cancers could pave the way for novel treatment strategies, leveraging its function in transcriptional regulation to inhibit tumor growth and progression.
